CVE-2025-10975 is a medium-severity vulnerability affecting the GuanxingLu vlarl software, specifically in the function experiments.robot.bridge.reasoning_server::run_reasoning_server located in the file experiments/robot/bridge/reasoning_server.py. The vulnerability arises from insecure deserialization of the Message argument within the ZeroMQ component. Deserialization vulnerabilities occur when untrusted data is deserialized without proper validation, allowing attackers to manipulate serialized objects to execute arbitrary code or cause denial of service. In this case, remote attackers can exploit this vulnerability without authentication or user interaction, by sending crafted messages to the reasoning server, triggering unsafe deserialization. The product uses a rolling release model, so no fixed version is specified beyond the affected commit 31abc0baf53ef8f5db666a1c882e1ea64def2997. Although an exploit has been publicly disclosed, there are no known exploits actively used in the wild at this time. The CVSS 4.0 vector indicates network attack vector (AV:N), low attack complexity (AC:L), no privileges required (PR:L - low privileges required), no user interaction (UI:N), and partial impact on confidentiality, integrity, and availability (VC:L, VI:L, VA:L). This suggests that an attacker with low privileges on the network can remotely exploit the vulnerability to partially compromise system confidentiality, integrity, and availability. The vulnerability is rooted in the ZeroMQ messaging framework integration, which is commonly used for asynchronous messaging in distributed systems, making it critical to address in environments relying on GuanxingLu vlarl for robotic or reasoning server applications.

For European organizations, the impact of this vulnerability depends on their use of GuanxingLu vlarl, particularly in robotics, automation, or AI reasoning server deployments. Exploitation could allow attackers to execute arbitrary code remotely, potentially leading to unauthorized access, data leakage, or disruption of critical automated processes. This could affect sectors such as manufacturing, logistics, research institutions, and any industry leveraging robotic automation or AI reasoning systems. Given the partial impact on confidentiality, integrity, and availability, attackers might manipulate reasoning outputs or disrupt robotic operations, leading to operational downtime or erroneous decision-making. The remote exploitability without user interaction increases the risk of automated attacks, especially in network-exposed deployments. European organizations with network-exposed GuanxingLu vlarl instances are at risk of targeted or opportunistic attacks, which could impact business continuity and data security.

1. Immediate mitigation involves restricting network access to the reasoning server component, ensuring it is not exposed to untrusted networks. 2. Implement network-level filtering and segmentation to isolate the affected service. 3. Apply strict input validation and sanitization on all serialized data received by the run_reasoning_server function to prevent malicious payloads. 4. Monitor network traffic for anomalous or unexpected serialized messages targeting the ZeroMQ component. 5. Since no official patch or fixed version is provided due to the rolling release model, coordinate with GuanxingLu developers or community to obtain updated commits addressing this vulnerability or implement custom patches to secure deserialization logic. 6. Employ runtime application self-protection (RASP) or application-layer firewalls capable of detecting and blocking deserialization attacks. 7. Conduct code audits focusing on deserialization usage and implement safer serialization libraries or patterns that enforce strict type checks. 8. Maintain up-to-date backups and incident response plans to quickly recover from potential exploitation.